Used in various fields such as mathematical finance and statistics. Two of the most common time series used for a data of sequence points is analysis and forecasting. Time series analysis is data points measured at successive times. While time series forecasting is estimating future events with given current data. This data processing is the most accurate from other data collecting processes. A time series is a sequence of observations taken sequentially in time.
There are many objectives for studying time series such as
understanding and describing its methodology and forecasting of future
values. A unique nature of time series is that its observations are
dependent or correlated. The statistical methodology for analyzing time
series is referred to as time series analysis. The five goals of time series data are: descriptive, explanation, forecasting, intervention analysis, and quality control. The two most common patterns used in time series is seasonality and common trends of data. There are numerous ways to locate and find a trend, but the most common is using a regression analysis. The other pattern used, seasonality is a trend that continuously repeats itself overtime in a systematical way. Time series has a unique approach in that in each observation, it is heavily influenced upon the next observation with strong correlation.
**Uses for Time Series** **Information**
A field that uses time series information is the Investment industry. Focusing on time-series forecasting, the opening of a stock price compared to its base price can help determine its past performance. An investment instrument used as an example would be a security. The time series aspect is to evaluate a securities performance over time relating it to similar variables at the same time. Everyday the stock market closes and the value of a security is documented. Now for 365 days of the year, each closing day is documented in chronological order. This is an example of an annual, daily time series for the stock. Other notable time series recollection would be the correlation how daily ending stock prices would be when there are economic factors involved such as unemployment, war, and potential natural disasters.
**Business Benefits**
There are many benefits for the usage of time series in many specific categories. Time series used for retail is beneficial for the sale history of the product sold. The forecast of inventory is used to optimize inventory levels. When a company has too much inventory, the expenses for the company rise significantly. When inventory is low, the opportunity for profit is endangered. Forecasting inventory is important and the utilization of time series is important. Manufactures also use time series. When analyzing the production history from year to year; this will help forecast upcoming years in manufacturing. This is very useful for anticipating production demand for future use. Lastly, time series can be used for customer service. The volume of calls per hour can be utilized by time series. This can be used to see if a company has enough staff correlated with its call volume.
**Software Programs **
The software programs used for time series are SPSS, JMP, and SAS/ETS.**See Also**
Time series regression analysis Investment industry stock
### References
http://www.stanford.edu/~wfsharpe/mat/tsi.htm http://www.imf.org/external/data.htm http://en.wikipedia.org/wiki/Time_series
http://elsa.berkeley.edu/sst/regression.html
http://en.wikipedia.org/wiki/File:Random-data-plus-trend-r2.png
http://www.spss.com/statistics/
http://www.jmp.com/software/
http://www.sas.com/index.html |